文章基本信息

标题：A Study of Gaussian Activation Function Based Modular Neural Network for Alternative-Style Handwritten Characters Recognition System
本地全文：下载
作者：Yuji WAIZUMI ; Tsutomu ISHII ; Nei KATO 等
期刊名称：Interdisciplinary Information Sciences
印刷版ISSN：1340-9050
电子版ISSN：1347-6157
出版年度：2001
卷号：7
期号：2
页码：189-196
DOI：10.4036/iis.2001.189
出版社：The Editorial Committee of the Interdisciplinary Information Sciences
摘要：We propose a design method using Gaussian activation function for alternative-style handwritten character recognition system. While the alternative method can gain high accuracy recognition performance by simplifying the recognition problem to linear discriminant problem, the overfitting problem will occur with small number of learning samples. In this paper, we introduce Gaussian function as activation function of neurons in order to avoid the overfitting problem. Our proposed method can learn the overall distribution of samples and gain higher generalization ability. In the recognition experiment using ETL9B 3036 categories, the proposed method can achieve 97.67% recognition accuracy.
关键词：handwritten character recognition;neural network;Gaussian activation function